548 research outputs found
Rethinking Performance Gains in Image Dehazing Networks
Image dehazing is an active topic in low-level vision, and many image
dehazing networks have been proposed with the rapid development of deep
learning. Although these networks' pipelines work fine, the key mechanism to
improving image dehazing performance remains unclear. For this reason, we do
not target to propose a dehazing network with fancy modules; rather, we make
minimal modifications to popular U-Net to obtain a compact dehazing network.
Specifically, we swap out the convolutional blocks in U-Net for residual blocks
with the gating mechanism, fuse the feature maps of main paths and skip
connections using the selective kernel, and call the resulting U-Net variant
gUNet. As a result, with a significantly reduced overhead, gUNet is superior to
state-of-the-art methods on multiple image dehazing datasets. Finally, we
verify these key designs to the performance gain of image dehazing networks
through extensive ablation studies
Fast Deep Multi-patch Hierarchical Network for Nonhomogeneous Image Dehazing
Recently, CNN based end-to-end deep learning methods achieve superiority in
Image Dehazing but they tend to fail drastically in Non-homogeneous dehazing.
Apart from that, existing popular Multi-scale approaches are runtime intensive
and memory inefficient. In this context, we proposed a fast Deep Multi-patch
Hierarchical Network to restore Non-homogeneous hazed images by aggregating
features from multiple image patches from different spatial sections of the
hazed image with fewer number of network parameters. Our proposed method is
quite robust for different environments with various density of the haze or fog
in the scene and very lightweight as the total size of the model is around 21.7
MB. It also provides faster runtime compared to current multi-scale methods
with an average runtime of 0.0145s to process 1200x1600 HD quality image.
Finally, we show the superiority of this network on Dense Haze Removal to other
state-of-the-art models.Comment: CVPR Workshops Proceedings 202
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